面向分布式数据安全共享的高速公路路网拥堵监测
Research on Expressway Network Congestion Monitoring for Secure Sharing of Distributed Data
李林锋 1陈羽中 2姚毅楠 2邵伟杰2
作者信息
- 1. 福建省高速公路联网运营有限公司,福建 福州 350000
- 2. 福州大学计算机与大数据学院,福建 福州 350116
- 折叠
摘要
应用人工智能技术对高速公路路网道路状态进行监测已成为热点,然而,数据孤岛及隐私保护是高速路网智能决策面临的挑战.为实现分布式数据安全共享及智能决策,以拥堵问题为例,提出基于联邦学习的高速路网道路拥堵状态监测策略.利用摄像头实时数据,在密态可计算的同态加密联邦学习智能决策架构下,建立基于道路区间优化的拥堵状态监测模型.结果表明,在确保分布式数据安全共享的前提下,能够有效实现高速路网道路拥堵状态监测.
Abstract
The application of artificial intelligence(AI)technology for monitoring the condi-tion of expressway networks has become a prominent research area.However,challenges such as data silos and privacy protection hinder intelligent decision-making in this domain.To address these issues and enable secure sharing of distributed data for intelligent decision-making,particularly with regard to congestion,a strategy based on federated learning is proposed.This strategy employs real-time camera data and utilizes a fully homomorphic encryption scheme within the federated learning framework.This enables the establishment of an encrypted,intelligent decision-making architecture to develop a congestion status monitoring model based on optimized road segments.The results indicate that,while ensuring the security and privacy of distributed data,this approach can effec-tively monitor expressway congestion.
关键词
高速公路路网/道路拥堵状态/数据安全共享/智能决策/联邦学习/同态加密Key words
expressway network/road congestion status/secure data sharing/intelligent deci-sion-making/federated learning/homomorphic encryption引用本文复制引用
出版年
2025
国家科技期刊平台
NSTL
万方数据